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Remote Sens. 2015, 7(9), 12215-12241; doi:10.3390/rs70912215

Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

1
Earth System Science Interdisciplinary Center at University of Maryland, College Park, MD 20740, USA
2
Center for Satellite Applications and Research, NOAA/NESDIS, College Park MD 20740, USA
3
Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen 76344, Germany
4
IPMA, Instituto Português do Mar e da Atmosfera, Lisboa 1749-077, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Changyong Cao, Richard Müller and Prasad S. Thenkabail
Received: 24 July 2015 / Revised: 8 September 2015 / Accepted: 14 September 2015 / Published: 21 September 2015
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
View Full-Text   |   Download PDF [6940 KB, uploaded 22 September 2015]   |  

Abstract

The VIIRS Land Surface Temperature (LST) Environmental Data Record (EDR) has reached validated (V1 stage) maturity in December 2014. This study compares VIIRS v1 LST with the ground in situ observations and with heritage LST product from MODIS Aqua and AATSR. Comparisons against U.S. SURFRAD ground observations indicate a similar accuracy among VIIRS, MODIS and AATSR LST, in which VIIRS LST presents an overall accuracy of −0.41 K and precision of 2.35 K. The result over arid regions in Africa suggests that VIIRS and MODIS underestimate the LST about 1.57 K and 2.97 K, respectively. The cross comparison indicates an overall close LST estimation between VIIRS and MODIS. In addition, a statistical method is used to quantify the VIIRS LST retrieval uncertainty taking into account the uncertainty from the surface type input. Some issues have been found as follows: (1) Cloud contamination, particularly the cloud detection error over a snow/ice surface, shows significant impacts on LST validation; (2) Performance of the VIIRS LST algorithm is strongly dependent on a correct classification of the surface type; (3) The VIIRS LST quality can be degraded when significant brightness temperature difference between the two split window channels is observed; (4) Surface type dependent algorithm exhibits deficiency in correcting the large emissivity variations within a surface type. View Full-Text
Keywords: VIIRS LST EDR; split window algorithm; surface type dependency; LST uncertainty VIIRS LST EDR; split window algorithm; surface type dependency; LST uncertainty
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, Y.; Yu, Y.; Yu, P.; Göttsche, F.M.; Trigo, I.F. Quality Assessment of S-NPP VIIRS Land Surface Temperature Product. Remote Sens. 2015, 7, 12215-12241.

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